Machine Learning Engineer – ML Experiences (MLX)

Requirements:

• Bachelors degree or higher in Computer Science, Machine Learning, or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• At least 4 years of programming experience with Python, Java, Golang, or C++.
• At least 4 years of machine learning experience with industry-standard frameworks such as PyTorch or TensorFlow and libraries such as Pandas, NumPy, or Scikit-learn.
• At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 2 years of experience deploying and operating machine learning solutions in production, running production services in a cloud environment (AWS, GCP, or Azure), and using Kubernetes to manage large-scale containerized machine learning systems.
• Preferred: Masters or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• Preferred: 3+ years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• Preferred: 3+ years of experience applying software development best practices, including source control, testing, code reviews, and CI/CD.
• Preferred: 3+ years of experience building resilient software solutions, including pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning.
• Preferred: 3+ years of experience with machine learning techniques, model types and architectures, training concepts, and evaluating model accuracy and diagnosing issues such as underfitting and overfitting.
• Preferred: 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Preferred: 1+ years of experience as a technical lead developing machine learning solutions using industry best practices, patterns, and automation.
• Preferred: Authorship or co-authorship of a paper on a machine learning technique, model, or proof of concept.

Responsibilities:

• Design, build, and deliver machine learning models and components to address business problems in collaboration with Product and Data Science teams.
• Apply knowledge of machine learning methods and challenges to infrastructure decisions, including model, data, and feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Develop and test application code, build and validate machine learning models, and automate testing and deployment to solve complex problems.
• Work within a cross-functional Agile team to create and improve software supporting advanced big data and machine learning applications.
• Retrain, maintain, and monitor production models.
• Use or develop cloud architectures, technologies, and platforms to deliver optimized machine learning models at scale.
• Build optimized data pipelines that supply machine learning models.
• Apply continuous integration and deployment practices, including automated testing and monitoring, to support successful deployment of models and application code.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI practices.
• Use programming languages such as Python, Scala, or Java.

Technologies:

• AI
• AWS
• Azure
• Big Data
• CI/CD
• Cloud
• GCP
• Golang
• Support
• Java
• Kubernetes
• Machine Learning
• PyTorch
• Python
• Scala
• Spark
• TensorFlow
• numpy
• pandas
• Model Training

More

Were hiring a Machine Learning Engineer 4 for our Machine Learning Experiences team at Capital One. We bring together makers, builders, and problem-solvers in a collaborative, inclusive, iterative environment, where this role will help advance AI initiatives and scale production models using open-source frameworks, algorithms, and emerging technologies. The role is available across the listed locations: Cambridge and Boston-area territory, Massachusetts; McLean and Richmond, Virginia; New York, New York; and San Jose, California. The listed annual salary ranges are $179,400–$204,700 for the sales territory and Richmond, $197,300–$225,100 for Cambridge and McLean, and $215,200–$245,600 for New York and San Jose. Pay for other locations varies; part-time pay is prorated, and the offer letter sets the salary offered. The role may also earn performance-based cash bonuses and/or long-term incentives. We offer a comprehensive benefits program supporting health, financial, and overall well-being, with eligibility varying by employment status and level. We do not provide new employment authorization sponsorship or immigration-related support for this position. We are an equal opportunity employer committed to non-discrimination and a drug-free workplace, and we consider applicants with criminal histories in accordance with applicable laws.

last updated 43 week of 2026